News Classifications


news Articles classification



Deploy a machine learning model to classify newspaper news

How to solve a classification problem

How to solve a classification problem - text classification, supervised learning. We’re going to design a machine learning model to classify a set newspaper headlines.



The problem

Users will collect examples of headlines from different newspapers in three categories (Sports, Politics, and Economy). These will be used to train a machine learning model based on text recognition techniques that put a newspaper on the correct shelf by predicting which headlines belong to which categories.

what is classification ?

Classification is a supervised learning technique in which the computer uses input data (in this project, newspaper news are the input data) to learn and then classify new observations. Further information can be found in the second course of Level2, Supervised Learning, in the AI Master Program. 


You will learn to

  • Collect data and split it into training and testing datasets. 
  • Build an AI model quickly and easily to:

Solve a classification problem by creating a Machine learning model to classify newspaper headlines to the correct category.  

  • Use your AI model

After training the model, you can deploy your experiment to a scratch project that puts a newspaper on the correct shelf based on the prediction of headlines.


The data

The dataset is comprised of dataset of newspapers outlines for 3 categories (Sports, Political and Economy) from the dataset library. This dataset will be used to train your ML model on how to do the classification task on its own . You will need to make sure that your split your data into training dataset and testing dataset so that you know how to assess the performance of your ML model. 


project steps: 


Meet your AI Identity. You will teach your AI how to complete tasks as a human would. In due time, your AI Identity will grow more intelligent as you train it some more to take on different tasks with multiple projects. Eventually -in the future- you will be able to transfer your AI identity's intellect into a physical robot you can interact with in the real world.  



First, create a project and name it so you know what kind of project it is. Naming is important. A project combines all of the steps in solving a problem, from the pre-processing of datasets to model building, evaluation, and deployment. Using projects makes it easy to collaborate with others. After adding the project’s name and description, click on CREATE



In this step, you will need to select one of the ML models in order to solve your problem, that is text classifying . You will find a computer vision model (Make Me See), Natural Language Processing model (Make Me Read), Voice Recognition (Make Me Hear), and a tabular model (Make Me Count). In our case, you will need to train your AI transformer to how to understand and classify newspaper news , therefore, you need to select MAKE ME READ. 

More AI capabilities (models) will be added to the platform, so stay tuned! These models will help you to accelerate the process of creating a trained AI model that solves your problem, and thus export into your app to start using it



This is the most important step in any ML project. In fact, data preprocessing is 70-80% of any ML project. In this section, you will need to manually collect newspaper news examples resources, such as Kaggle, or import News classification dataset from our data library



Right now, and after you completed training your model, you are ready to test it using a news sample text. You can test your model by typing any text.

Just make sure that you don’t use an example from the dataset that you used in the training part, otherwise, you are cheating! Upload or import the sample news to assess the performance in the preview section. If you like the confidence score, you can export your model. Otherwise, go back to the "collect data" step and check whether you have used sufficient number of news in the training dataset. 

Click TRAIN ME to start the training. This may take a few moments.



If you are happy with the testing results, you can "Export" your ML model using our API into your own websiteapplication or even to your robot or any machine! If you are not happy with the confidence score, you will need to check your training data classes again, and make sure that you have clean and sufficient number of examples for each data class. Keep in mind the concept of GIGO (garbage in, garbage out) which means that the quality of your ML model output is determined by the quality of your input, i.e. training dataset.